Bayesian Inference for Spatial Beta Generalized Linear Mixed Models [PDF]
Journal of Sciences, Islamic Republic of Iran, 2018 In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model
L. Kalhori Nadrabadi, M. Mohhamadzadeh
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Journal of Plant Ecology, 2023 glmm.hp is an R package designed to evaluate the relative importance of collinear predictors within generalized linear mixed models (GLMMs). Since its initial release in January 2022, it has rapidly gained recognition and popularity among ecologists ...
Jiangshan Lai +3 more
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Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation [PDF]
, 2011 Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates.
Groll, Andreas
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An adjusted coefficient of determination (R2) for generalized linear mixed models in one go
Biometrical journal. Biometrische Zeitschrift, 2023 The coefficient of determination (R2) is a common measure of goodness of fit for linear models. Various proposals have been made for extension of this measure to generalized linear and mixed models.
H. Piepho
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Comparison of predictor approaches for longitudinal binary outcomes: application to anesthesiology data [PDF]
PeerJ, 2014 Longitudinal data with binary repeated responses are now widespread among clinical studies and standard statistical analysis methods have become inadequate in the answering of clinical hypotheses.
Anil Aktas Samur +2 more
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Laplace approximation, penalized quasi-likelihood, and adaptive Gauss-Hermite quadrature for generalized linear mixed models: towards meta-analysis of binary outcome with sparse data. [PDF]
BMC Med Res Methodol, 2020 Background In meta-analyses of a binary outcome, double zero events in some studies cause a critical methodology problem. The generalized linear mixed model (GLMM) has been proposed as a valid statistical tool for pooling such data.
Ju K, Lin L, Chu H, Cheng LL, Xu C.
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Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting [PDF]
, 2011 With the emergence of semi- and nonparametric regression the generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed
Groll, Andreas, Tutz, Gerhard
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Generalized Linear Mixed Models with Gaussian Mixture Random Effects: Inference and Application. [PDF]
J Multivar Anal, 2020 Pan L, Li Y, He K, Li Y, Li Y.
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To transform or not to transform: using generalized linear mixed models to analyse reaction time data. [PDF]
Front Psychol, 2015 Linear mixed-effect models (LMMs) are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by Speelman and McGann (2013) about the use of mean data ...
Lo S, Andrews S.
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Generalized fiducial inference for normal linear mixed models [PDF]
, 2012 While linear mixed modeling methods are foundational concepts introduced in any statistical education, adequate general methods for interval estimation involving models with more than a few variance components are lacking, especially in the unbalanced ...
Cisewski, Jessi, Hannig, Jan
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